Salvatore Filippone | Cranfield University (original) (raw)
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Papers by Salvatore Filippone
Parallel Computing, Mar 1, 2018
Parallel Computing, Apr 1, 2016
Parallel Computing, 2013
The popularity of GPGPUs in high performance platforms for scientific computing in recent times h... more The popularity of GPGPUs in high performance platforms for scientific computing in recent times has renewed interest in approximate inverse preconditioners for Krylov methods. We have recently introduced some new algorithmic variants [6] of popular approximate inverse methods. We now report on the behaviour of these variations in high performance multilevel preconditioning frameworks, and we present the software framework that enables
Numerical Linear Algebra with Applications
We consider here a cell‐centered finite difference approximation of the Richards equation in thre... more We consider here a cell‐centered finite difference approximation of the Richards equation in three dimensions, averaging for interface values the hydraulic conductivity , a highly nonlinear function, by arithmetic, upstream and harmonic means. The nonlinearities in the equation can lead to changes in soil conductivity over several orders of magnitude and discretizations with respect to space variables often produce stiff systems of differential equations. A fully implicit time discretization is provided by backward Euler one‐step formula; the resulting nonlinear algebraic system is solved by an inexact Newton Armijo–Goldstein algorithm, requiring the solution of a sequence of linear systems involving Jacobian matrices. We prove some new results concerning the distribution of the Jacobians eigenvalues and the explicit expression of their entries. Moreover, we explore some connections between the saturation of the soil and the ill conditioning of the Jacobians. The information on eige...
Computers & Mathematics with Applications
In this paper we describe some work aimed at upgrading the Alya code with up-to-date parallel lin... more In this paper we describe some work aimed at upgrading the Alya code with up-to-date parallel linear solvers capable of achieving reliability, efficiency and scalability in the computation of the pressure field at each time step of the numerical procedure for solving a LES formulation of the incompressible Navier-Stokes equations. We developed a software module in the Alya's kernel to interface the libraries included in the current version of PSCToolkit, a framework for the iterative solution of sparse linear systems on parallel distributed-memory computers by Krylov methods coupled to Algebraic MultiGrid preconditioners. The Toolkit has undergone some extensions within the EoCoE-II project with the primary goal to face the exascale challenge. Results on a realistic benchmark for airflow simulations in wind farm applications show that the PSCToolkit solvers significantly outperform the original versions of the Conjugate Gradient method available in the Alya kernel in terms of sc...
2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
arXiv (Cornell University), Dec 9, 2021
arXiv (Cornell University), Oct 29, 2022
arXiv (Cornell University), Jan 27, 2020
Software impacts, Mar 1, 2023
Neural Computing and Applications, 2022
The importance of robust flight delay prediction has recently increased in the air transportation... more The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network...
Parallel Computing, Mar 1, 2018
Parallel Computing, Apr 1, 2016
Parallel Computing, 2013
The popularity of GPGPUs in high performance platforms for scientific computing in recent times h... more The popularity of GPGPUs in high performance platforms for scientific computing in recent times has renewed interest in approximate inverse preconditioners for Krylov methods. We have recently introduced some new algorithmic variants [6] of popular approximate inverse methods. We now report on the behaviour of these variations in high performance multilevel preconditioning frameworks, and we present the software framework that enables
Numerical Linear Algebra with Applications
We consider here a cell‐centered finite difference approximation of the Richards equation in thre... more We consider here a cell‐centered finite difference approximation of the Richards equation in three dimensions, averaging for interface values the hydraulic conductivity , a highly nonlinear function, by arithmetic, upstream and harmonic means. The nonlinearities in the equation can lead to changes in soil conductivity over several orders of magnitude and discretizations with respect to space variables often produce stiff systems of differential equations. A fully implicit time discretization is provided by backward Euler one‐step formula; the resulting nonlinear algebraic system is solved by an inexact Newton Armijo–Goldstein algorithm, requiring the solution of a sequence of linear systems involving Jacobian matrices. We prove some new results concerning the distribution of the Jacobians eigenvalues and the explicit expression of their entries. Moreover, we explore some connections between the saturation of the soil and the ill conditioning of the Jacobians. The information on eige...
Computers & Mathematics with Applications
In this paper we describe some work aimed at upgrading the Alya code with up-to-date parallel lin... more In this paper we describe some work aimed at upgrading the Alya code with up-to-date parallel linear solvers capable of achieving reliability, efficiency and scalability in the computation of the pressure field at each time step of the numerical procedure for solving a LES formulation of the incompressible Navier-Stokes equations. We developed a software module in the Alya's kernel to interface the libraries included in the current version of PSCToolkit, a framework for the iterative solution of sparse linear systems on parallel distributed-memory computers by Krylov methods coupled to Algebraic MultiGrid preconditioners. The Toolkit has undergone some extensions within the EoCoE-II project with the primary goal to face the exascale challenge. Results on a realistic benchmark for airflow simulations in wind farm applications show that the PSCToolkit solvers significantly outperform the original versions of the Conjugate Gradient method available in the Alya kernel in terms of sc...
2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
arXiv (Cornell University), Dec 9, 2021
arXiv (Cornell University), Oct 29, 2022
arXiv (Cornell University), Jan 27, 2020
Software impacts, Mar 1, 2023
Neural Computing and Applications, 2022
The importance of robust flight delay prediction has recently increased in the air transportation... more The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network...